CN109343505A - Gear method for predicting residual useful life based on shot and long term memory network - Google Patents

Gear method for predicting residual useful life based on shot and long term memory network Download PDF

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Publication number
CN109343505A
CN109343505A CN201811096152.0A CN201811096152A CN109343505A CN 109343505 A CN109343505 A CN 109343505A CN 201811096152 A CN201811096152 A CN 201811096152A CN 109343505 A CN109343505 A CN 109343505A
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gear
lstm
input
cell
gradient
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石慧
杨勇
刘佳媛
王婉娜
申继发
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Taiyuan University of Science and Technology
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Taiyuan University of Science and Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/021—Gearings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G01M13/028—Acoustic or vibration analysis

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Acoustics & Sound (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The present invention is based on the gear method for predicting residual useful life of shot and long term memory network to belong to big data and intelligence manufacture field, it solves the problems, such as to rely on phenomena such as being easy to appear gradient explosion or gradient dispersion with Recognition with Recurrent Neural Network when existing RNN algorithm can not solve long, using algorithm, specific step is as follows: 1, using sensor to power gear degeneration real-time monitoring；2, feature extraction is carried out to gear fatigue state, slump evaluations is carried out to gear wear degraded performance；3, the Real-time Monitoring Data prediction model of the tooth bending fatigue based on LSTM is established；4, the parameter based on LSTM prediction model is preferred；5, gear predicting residual useful life is carried out according to gear condition estimation and known gear distress threshold value and provides foundation it is an advantage of the invention that the accuracy of gear degenerate state and real-time remaining life can be predicted effectively for gear prospective maintenance.

Description

Gear method for predicting residual useful life based on shot and long term memory network
Technical field
The invention belongs to big datas and intelligence manufacture field, more particularly to a kind of gear based on shot and long term memory network Method for predicting residual useful life.
Background technique
Gear-box is the machinery field such as main mechanical part of cluster engine, wind power system, gear drive be even more along with Entire steel industry.With the expansion of modern machinery and equipment collection on a large scale, the continuous improvement of precision, long-term operation middle gear very It is easy to appear that attrition causes the gear teeth thinning or stress is concentrated, tooth root decrease of fatigue strength reduces gear life in turn, weighs Carry lower even generation break of gear tooth.The health status of monitoring gear is to ensure mechanical equipment reliability service important means.
Gear is generally enclosed in gear-box, the voice signal and vibration signal real-time monitoring received by sensing equipment Gear health state, but the characteristic quantity for generating the vibration signal of failure has extremely complex non-linear relation and enchancement factor. The data that characterization equipment degenerate state is obtained by monitoring carry out fault identification and pre- by data processing and feature extraction again It surveys.Failure is identified and predicted by model and method, realizes the life prediction of mechanical equipment.
In recent years, with the continuous development of artificial intelligence technology, deep learning is also had received widespread attention.Deep learning Essence is the neural network model for having many hidden layers by the training data building of magnanimity, the feature by sample in former space Expression transforms to a new feature space, successively extracts potential mapping relations in input and output sample.Recognition with Recurrent Neural Network (Recurrent NeuralNetworks, RNN) is chiefly used in the processing of sequence data, can remember to the sequence inputting of front And in the calculating after being applied to.RNN uses Back-propagation Through Time after completing layer-by-layer pre-training (BPTT) residual error of accumulation is passed back and completes parameter regulation by algorithm.Dependence when BPTT can not solve long is found in practical application Problem, i.e., current output is related with one section of very long sequence of front, is easy to bring gradient disappearance or gradient explosion issues.Length Phase remembers (long short-term memory, LSTM) network model and passes through the gate of more parameters and control information flow Cellular system can avoid the problems such as gradient is disappeared with gradient explosion, so that timing information prediction is more accurate.
Summary of the invention
The present invention overcomes the shortcomings of the prior art, what is solved existing BPTT algorithm and rely on when can not solve long is asked Topic and Recognition with Recurrent Neural Network are easy to appear phenomena such as gradient explosion or gradient dispersion, it is desirable to provide one kind is based on modified length The gear method for predicting residual useful life of phase memory network, the present invention include three layers of (input layer, hidden layer and output layer) network knot The detailed design of structure and the realization algorithm of network training and neural network forecast etc..On this basis, mould is selected using random search Type hyper parameter is added that momentum considers and builds from network structure, improved model is accurate in the settings such as learning rate, window size Property, it prevents from falling into local optimum, improves model prediction accuracy.
In order to solve the above technical problems, the technical solution adopted by the present invention are as follows: based on modified shot and long term memory network Gear method for predicting residual useful life, including following implementation steps:
Step 1 installs sensor in gear-box, obtains the Real-time Monitoring Data of tooth bending fatigue；
Acceleration transducer is mounted on the bearing block position of main examination case, the mounting temperature sensor in gear-box, in main examination Noise transducer is installed in the surface of case,
Step 2 carries out feature extraction to the fatigue state of gear, carries out slump evaluations to gear wear degraded performance；
Gear wear condition is changed using square amplitude and carries out performance slump evaluations, for each sampling time Δ t long In degree, the square amplitude of the time series of discrete random signal be may be expressed as:
Δ t is using the time in formula；N=Fs× Δ t, FsFor sample frequency, n is sampling number, and ∑ indicates summation, i ∈ (1,2,3....n), xiIt (t) is sampled value；
Step 3, the Real-time Monitoring Data prediction model for establishing the tooth bending fatigue based on LSTM:
The present invention constructs the general frame of LSTM prediction model as shown in Figure 1, including input layer, hidden layer, output layer, net Five functional modules of network training and neural network forecast.Input layer is responsible for carrying out the Real-time Monitoring Data of tooth bending fatigue preliminary To meet network inputs requirement, hidden layer builds single layer Recognition with Recurrent Neural Network, output layer using the LSTM cell that Fig. 2 is indicated for processing Prediction result is provided, network training uses building from network structure, optimizes, join to LSTM on learning rate, window setting Nesterov momentum is added when number amendment, prevents from falling into local optimum, using the excellent of RMSProp algorithm correction model adaptive rate Change method, neural network forecast uses the method prediction point-by point of iteration, and then improves the accuracy of network model prediction.
LSTM recirculating network as shown in Figure 2 (replaces general circulation nerve at itself " cell " other than RNN formula circulation The hidden unit of network) inside circulation.Each unit phase input and output, but also have more parameters and control information flow Door control unit system, i.e. state cell(moment t, cell i), external input door (external input gate) forget door (forget gate), out gate (output gate).Forget door to be responsible for removing the unessential information of LSTM study from " cell ", These information will be removed by door control unit operation.Forget door and takes two input ht-1And xt。ht-1For the state of previous unit Value, xtFor the value of the t moment of list entries x.Add bias term again to input Sigmoid function.Sigmoid function value range For [0,1], if value is zero, forgetting door allows for location mode and forgets the information.Corresponding propagated forward algorithm is as follows:
Input gate:
Forget door:
Cell:
Out gate:
Cell output:
Backpropagation is calculated as shown in (8)-(14):
Cell output:
Out gate:
State:
Cell:
Forget door:
Input gate:
Wherein ∑ indicates summation,It is current input vector, subscript l, φ, c, ω, which is respectively indicated, to be indicated input gate, lose Forget the relevant parameter of door, cell state unit, out gate.wil、wiφ、wicAnd wiwRespectively indicate from be input to input gate, forget The weight of door, cell state unit, out gate, whl、whφ、whcAnd whwIt respectively indicates from hidden layer to input gate, forget door, cell The weight of state cell, out gate, wcl、wcφAnd wcwIt respectively indicates from cell state to input gate, the power of forgetting door and out gate Weight, atIndicate the input of t moment, btIndicate the output of t moment,Indicate the state of t moment cell, g and h respectively indicate cell Input and output activation primitive, I indicate input layer neuron number, K indicate output layer neuron number, H indicate it is hidden The number of cells of layer, σ is sigmoid function.Indicate the cell output of t moment,Indicate the state of t moment, ζ indicates loss Function, δtIndicate gradient error, wckAnd wcgIt respectively indicates from cell state to output layer and the weight of hidden layer,
Network training
First in input layer, the Real-time Monitoring Data that we define tooth bending fatigue is F0={ f1,f2,…,fn, The training set and test set then divided can be expressed as Ftr={ f1,f2,…,fmAnd Fte={ fm+1,fm+2,…,fn, meet about Beam condition m < n and m, n ∈.Then to the element f in training settIt is standardized, is standardized using classical z-score public Formula (mean value 0, standard deviation 1, be expressed as zscore), the training set after standardization can indicate are as follows:
Ftr'={ f1',f2',…,fm'} (15)
Meet:
1≤t≤m,t∈ (17)
The characteristics of in order to adapt to hidden layer input, we apply the method f ' of data segmentationtrIt is handled, sets segmentation window Mouth length value is L, then the mode input after dividing are as follows:
X={ X1,X2,…,XL} (18)
Xp={ fp',fp+1',…,fm-L+p-1'} (19)
Meet:
1≤p≤L；p,L∈N (20)
Corresponding output are as follows:
Y={ Y1,Y2,…YL} (21)
Yp={ fp+1',fp+2',…,fm-L+p'} (22)
Next, X is inputted hidden layer by us.It will be seen from figure 1 that hidden layer includes L by the connection of front and back moment Isomorphism LSTM cell, the output after hidden layer can indicate are as follows:
P={ P1,P2,…,PL} (23)
Pp=LSTMforward(Xp,Cp-1,Hp-1) (24)
Wherein, Cp-1And Hp-1The state and output of respectively previous LSTM cell, LSTMforwardBefore indicating LSTM cell To calculation method (formula (2)-(7)).Cell state vector magnitude is set as Sstate, then Cp-1And Hp-12 vector magnitudes are Sstate.As can be seen that hidden layer P, mode input X and theoretical output Y are the two-dimensional arrays that dimension is (m-L, L).It selects equal For square error as error calculation formula, the loss function of training process can be with is defined as:
The minimum optimization aim of loss function is set, the train epochs steps of netinit is given, using RMSProp Change learning rate, Nesterov introduces momentum and changes gradient, changes update mode in terms of two, and then obtain final hidden layer Network.
The present invention optimizes loss function using stochastic gradient descent (Stochasitc Gradient Descent, SGD). Gradient descent method will minimize loss on whole training datas, can be very when sample size is very big or the number of iterations increases Consume computing resource.Stochastic gradient descent generates distribution according to data and extracts m small lot (independent identically distributed) sample, each Secondary iteration only calculates the loss of a sample, then traverses all samples again, completes the calculating of a wheel.By calculating their ladder Mean value is spent, the unbiased esti-mator of available gradient is expressed as follows
Wherein g is gradient, and θ is the initial parameter of LSTM model forward calculation,It is gradient element, table ShowLocal derviation is asked to θ, using the sample from small lot m, stochastic gradient descent algorithm uses following gradient Decline estimation
θ←θ-ηg (27)
Wherein η is learning rate, and L is the loss of each sample
L (x, y, θ)=- logp (y | x；θ) (28)
Wherein, logarithm is sought in log expression.
Crucial parameter is learning rate in SGD algorithm, can gradually change study over time in LSTM application Rate.
Momentum parameter optimization
In deep learning, stochastic gradient descent is the optimization algorithm being in daily use, and optimization process is very slow sometimes, therefore Momentum optimization algorithm is added in the present invention to be intended to accelerate to learn, especially to the gradient of plus noise.Momentum is in physically table Speed is shown as multiplied by quality, in momentum learning algorithm, it is assumed that be unit mass, therefore velocity vector v can regard the dynamic of particle as Amount.The inertia for simulating object, gradient is added up, that is, increases stability to a certain extent when update, thus accelerate to learn, Inhibit concussion, there is the ability for jumping out local optimum, be expressed as
The contribution decaying of gradient, is set as 0.5,0.9,0.99 before hyper parameter α ∈ [0,1] is determined.If Error function Gradient g is always observed, then it can ceaselessly accelerate on-g, until reaching final speed.Wherein step sizes are
Wherein momentum hyper parameter isWhen α=0.9,10 times of gradient descent algorithms of maximum speed are corresponded to.Make herein With the stochastic gradient descent of Nesterov momentum, error is calculated after parameter Estimation is complete, is implemented as follows shown:
Assuming that learning rate η, momentum parameter α, initial rate v
It is iterated process every time:
1. extracting the small lot sample of { x (1), x (2) ... ..x (m) } from training set, wherein xtCorresponding output yt,
2. calculate gradient and Loss, v and θ are updated with this:
Calculate gradient:
It is interim to update:
θ←θ+v
Difference between Nesterov momentum and standard momentum is embodied in gradiometer and counts in, gradiometer in Nesterov momentum It calculates after applying present speed, it is optimal in conjunction with original gradient searching, it may be interpreted as being added to one into standard momentum method Correction factor.
Variable learning rate arithmetic
Learning rate has a significant impact the performance of model, determines that parameter is moved to the speed of optimal value.Amplitude is excessive, meeting Cause parameter that may cross optimal value；Amplitude is too small, easily causes operation redundancy, and long-time operation can not restrain.The present invention adopts It with the RMSProp algorithm adaptively changing learning rate of Nesterov momentum, acquires optimal value and obtains while improving convergence rate, update It is as follows:
Assuming that learning rate η, momentum parameter α, initial rate v, rate of decay ρ initialize cumulant variable γ=0
It is iterated process every time:
1. extracting the small lot sample of { x (1), x (2) ... ..x (m) } from training set, wherein xtCorresponding output yt,
2. calculating gradient and Loss, γ is updated, calculates and updates in conjunction with gradient:
Calculate gradient:
It is interim to update:
θ←θ+v
RMSProp changes learning rate, and Nesterov introduces momentum and changes gradient, changes update mode in terms of two.Every time After iteration, the value of loss function is checked using model parameter.If being less than last iteration, increase learning rate；Conversely, reducing study Rate.
Neural network forecast
We (are expressed as application enhancements type LSTM network) predicted.The method that prediction process uses iteration. Firstly, the last line data of output Y are as follows:
Yf={ fm-L+1',fm-L+2',…,fm'} (31)
Y is inputtedOutput result can indicate are as follows:
Then the predicted value at m+1 moment is pm+1.Then, by YfRear L-1 data point and pm+1Merge into a new line number According to:
Yf+1={ fm-L+2',fm-L+3',…,pm+1} (33)
By Yf+1InputThen the predicted value at m+2 moment is pm+2, and so on, obtained forecasting sequence are as follows:
Po={ pm+1,pm+2,…pn}(34)
Next, by P0It carries out z-score instead to standardize and (be expressed as de_zscore), obtain final with test Collect FteCorresponding forecasting sequence are as follows:
Wherein: m+1≤k≤n, k ∈ N.
Similarly, available with training set F using every a line of X as mode inputtrCorresponding fitting sequence Ptr.Most Afterwards, by calculating FtrAnd PtrAnd FteAnd PteDeviation quantitatively provide fitting and the precision of prediction of model.
Step 4, the parameter based on LSTM prediction model are preferred:
Deep learning algorithm all controls network parameter with many hyper parameters, and the optimization algorithm of automatic hyper parameter does not need to make Determine the hyper parameter of learning algorithm.
It include many parameters in LSTM prediction model, wherein split window, state vector size is closed the most with learning rate Key.Then, these hyper parameter cartesian products obtain one group of super parameter.Grid search uses a determining compensation, just Then change each step on coefficient and learning rate and take a value, the present invention can be found that data are one-dimensional at certain by using random search On variation it is more obvious, obtain apparent trend.
Random search programming is simple, using more convenient, can converge to the good value of hyper parameter faster.With grid search Difference, random search do not need the value of discretization hyper parameter, and permission scans in a bigger set, without generating Additional calculating cost.
The present invention uses random search, is that good hyper parameter can be quickly found because of it, the experiment not wasted, unlike Grid search provides identical result to two different values of a hyper parameter (it is constant to give other hyper parameter values) sometimes.In net In lattice search, other hyper parameters will possess identical value in this is tested twice, and in random search, they would generally have Different values.Therefore, if the verifying collection error corresponding to the variation of the two values does not have significant difference, grid search does not have It is necessary to repeat two experiments of equal value, and random search still can independently explore other hyper parameters twice.
Step 5 carries out gear predicting residual useful life according to gear condition estimation and known gear distress threshold value:
Data of the vibration data that acceleration transducer receives after feature extraction are as mode input, using improvement Type LSTM model carries out gear predicting residual useful life.Pass through the degenerate state value predicted and known degenerate state fault threshold The time for reaching fault threshold for the first time can be solved.Sampling number is converted to the time, absolute error percentage (Absolute is quoted Percentage Error,APE)
Wherein, TRULFor the remaining life of model prediction,
Ta=T*-tp(38)
In formula, tpIndicate current time, TaFor the real surplus life-span of gear, T*Fault threshold is reached for the first time for gear Time.
The invention has the advantages that
The present invention proposes a kind of real-time method for predicting residual useful life of gear, and the present invention is established for gear wear degenerative process The real-time predicting residual useful life model of modified LSTM based on memory mechanism under a kind of deep learning frame, and from network structure It builds, learning rate, window setting is upper optimizes to LSTM, and addition Nesterov momentum when parameters revision prevents from falling into part It is optimal, using RMSProp algorithm correction model adaptive rate, and then improve the accuracy of network model prediction.
Detailed description of the invention
The present invention will be further described in detail with reference to the accompanying drawing.
Fig. 1 is the real-time method for predicting residual useful life flow chart of middle gear of the embodiment of the present invention；
Fig. 2 is LSTM structure chart in the embodiment of the present invention；
Fig. 3 is the square amplitude curve figure of characteristics extraction that No. 4 sensors receive data in the embodiment of the present invention；
Fig. 4 is to carry out gear predicting residual useful life effect picture using modified LSTM model in the embodiment of the present invention；Fig. 4 It (a) is 70 hours prediction effect figures, Fig. 4 (b) is 73.3 hours prediction effect figures, and Fig. 4 (c) is 75 hours prediction effect figures；
Fig. 5 is LSTM training error decline curve figure in the embodiment of the present invention；
Fig. 6 is grid search and random search figure in the embodiment of the present invention.
Specific embodiment
It is understandable to enable objects, features and advantages of the present invention to become apparent, with reference to the accompanying drawing to tool of the invention Body embodiment is described in detail.
In the embodiment of the present invention, the real-time method for predicting residual useful life of gear, method flow diagram include as shown in Figure 1: with Lower step:
Step 1 carries out fatigue test to gear, obtains the Real-time Monitoring Data that characterization gear is degenerated:
Gear fatigue life test uses power flow blocking test rack.The center of testing stand is away from for 150mm, motor Revolving speed is 1200r/min.Test process is monitored cabinet vibration, oil temperature and noise etc..Use material for alloy in test Steel, tooth face hardness are the hardened face gear of 58-61HRC, are surface-treated as carburizing and quenching.Engagement side is staggeredly overlapped using front and back sides Formula.Main examination case module m=3, number of teeth z1=z2=50, pressure angle α=20 °, facewidth 29mm, the real work facewidth 13~ 14mm；Accompanying examination case number of gear teeth is z3=z4=24, and pressure angle α=20 °, work facewidth 20mm.Gear mesh frequency has 2, Main examination case gear is 1000Hz, and accompanying examination case gear is 480Hz.It tests lubricating oil and uses L-CKC320 Industrial Closed gear oil；
11 sensors are arranged in test altogether, and acceleration transducer is mounted on the bearing block position of main examination case, in gear-box Mounting temperature sensor installs noise transducer in the surface of main examination case.No. 1~No. 8 sensors are acceleration transducer, 1 Number~No. 4 sensor arrangements in the radial direction of main examination axle box bearing seat, No. 7 and No. 8 sensor arrangements are in the main axial direction for trying case, No. 5 and 6 Number sensor arrangement is in the radial direction for accompanying examination axle box bearing seat；No. 9 and No. 10 sensors are sonic transducer, be arranged in main examination case with It accompanies right above examination case at about 40cm；No. 11 sensors are the temperature sensors for testing lubricating oil temperature, are arranged in main examination cabinet, Lubricating oil temperature is tested in test.Sample frequency 25.6kHz, sampling time 60s, sampling interval 9min, using normal in this test Method, that is, dead load mode carries out rule in groups, torque 822.7N.M, determines the gear failure when testing gear and broken teeth occurring；
Step 2 carries out feature extraction to the fatigue state of gear, carries out slump evaluations to gear wear degraded performance；
Detachment tooth position in gear fatigue test is selected to set recently and in bearing block location arrangements when carrying out life prediction 463 groups of vibration signals of No. 4 sensors output carry out feature extraction.Test sample frequency 25.6kHz, sampling time 60s, sampling It is spaced 9min.Sampled point number, which is converted, just can be obtained shown in Fig. 34 by formula (1) after removing singular point for monitoring time The characteristic value of number sensor output is with monitoring time change curve.
The square amplitude curve of No. 4 sensors is contained from beginning running in stage to completion fatigue test at 77.2h in Fig. 3 The square amplitude of vibration signal that broken teeth occurs, square amplitude y when broken teeth known to gear fatigue test*(T*)=77.375, are denoted as Degradation, T*For the time for reaching fault threshold for the first time, T*=77.2h；
Step 3, the Real-time Monitoring Data prediction model for establishing the tooth bending fatigue based on LSTM:
Data of the vibration data that acceleration transducer receives after feature extraction are as mode input, the data set 463 group data of the gear from operating normally to breaking down are contained, preceding 300 groups of data is selected to be used for model training, 163 groups of numbers According to for model measurement.First 300 groups of formula (16) the standardization tooth bending fatigue for training set in application network training Real-time Monitoring Data, f 'trFor the training set after standardization, it is after dividing that preliminary setting split window length L, which is minimized 2, X, Mode input, corresponding output is Y, X is inputted hidden layer, by propagated forward (2) formula-(7) formula, obtained output result is P.To minimize loss function formula (25) as target, learning rate is changed using RMSProp, Nesterov introduces momentum and changes ladder The method of degree optimizes LSTM model.We (are expressed as application enhancements type LSTM network) predicted.Prediction process is adopted With the method for iteration.Firstly, the last line data of output Y are as follows:
Yf={ fm-L+1',fm-L+2',…,fm'} (31)
Y is inputtedOutput result can indicate are as follows:
Then the predicted value at m+1 moment is pm+1.Then, by YfRear L-1 data point and pm+1Merge into a new line number According to:
Yf+1={ fm-L+2',fm-L+3',…,pm+1} (33)
By Yf+1InputThen the predicted value at m+2 moment is pm+2, and so on, obtained forecasting sequence are as follows:
Po={ pm+1,pm+2,…pn} (34)
Next, by P0It carries out z-score instead to standardize and (be expressed as de_zscore), obtain final with test Collect FteCorresponding forecasting sequence are as follows:
Wherein: m+1≤k≤n, k ∈ N.
Similarly, available with training set F using every a line of X as mode inputtrCorresponding fitting sequence Ptr.Most Afterwards, by calculating FtrAnd PtrR and FteAnd PteDeviation quantitatively provide fitting and the precision of prediction of model.
Prediction result is as shown in Figure 4.Abscissa is the sample number of acquisition, and ordinate indicates the fitting of the network model and pre- Survey ability carries out the prediction of degenerate state in 70h, 73.3h, 75h respectively, and the defeated of models fitting is indicated before predicted time point Data out indicate prediction result after predicted time point.
As frequency of training step=200, the training error loss of model is as shown in figure 5, abscissa is the training time of model Number, ordinate indicate training error loss.Know the increase with frequency of training, loss is on a declining curve, shows model Learn the forecasting problem and has predictive ability.LSTM starts to restrain at iteration 300 times or so, and decrease speed is slack-off, explanation Its error is very small, has good predictive ability.The fitting effect of LSTM model is good, the energy of learning data development law Power is strong, and it is as shown in table 1 below that sampling number corresponds to the difference between actual value and predicted value.
1 prediction data of table and truthful data compare
Step 4, the parameter based on LSTM prediction model are preferred:
It include many parameters in LSTM prediction model, wherein split window, state vector size is closed the most with learning rate Key.Then, these hyper parameter cartesian products obtain one group of super parameter.The present invention uses random search, can faster reduce and test Error is demonstrate,proved, as shown in fig. 6, variation of the discovery data on certain is one-dimensional is more obvious, obtains apparent trend.
Random search programming is simple, using more convenient, can converge to the good value of hyper parameter faster.Grid search makes With a determining compensation, each step takes a value on regularization coefficient and learning rate.It is different from grid search, it searches at random Rope does not need the value of discretization hyper parameter, and permission scans in a bigger set, without generating additional calculating Cost.
The present invention uses random search, is that good hyper parameter can be quickly found because of it, the experiment not wasted, unlike Grid search provides identical result to two different values of a hyper parameter (it is constant to give other hyper parameter values) sometimes.In net In lattice search, other hyper parameters will possess identical value in this is tested twice, and in random search, they would generally have Different values.Therefore, if the verifying collection error corresponding to the variation of the two values does not have significant difference, grid search does not have It is necessary to repeat two experiments of equal value, and random search still can independently explore other hyper parameters twice.
Step 5 carries out gear predicting residual useful life according to gear condition estimation and known gear distress threshold value:
Data of the vibration data that acceleration transducer receives after feature extraction are as mode input, using improvement Type LSTM model carries out gear predicting residual useful life.Pass through the degenerate state value predicted and known degenerate state fault threshold The time for reaching fault threshold for the first time can be solved.Sampling number is converted to the time, absolute error percentage (Absolute is quoted Percentage Error, APE), TRULFor the remaining life of model prediction.
As can be seen from the above embodiments:
Know that the degenerate state curve predicted in different moments LSTM model is different as shown in Figure 4, it is pre- at 70 hours Survey state value deviation actual value is more, and error is larger.As monitoring information increases, the degenerate state of LSTM model prediction is connect Nearly true value, error reduce, can well forecasting system degenerate state mutate after its degenerate state variation and failure Time of origin.By Fig. 4 (c) it can be seen that when just detecting mutation status point, based on modified LSTM prediction model than very fast Close to estimative time of day, its dynamic change can be tracked well.
The accurate of remaining life is predicted for a kind of prediction technique of the further real-time remaining life of gear more of the invention Property, introduce the concept of prediction accuracy, prediction accuracy PA are as follows:
Wherein | TRUL-Ta| indicate absolute value, TRULFor the remaining life of model prediction, TaFor gear real surplus life-span, T* =77.2h is the gear physical fault time, then Ta=T*-tp, thus can calculate the comparison such as table 2 of LSTM model prediction accuracy It is shown:
The comparison of 2 model prediction accuracy of table
The invention patent training sample data as seen in Table 2 are more, and predictablity rate is higher.For fatigue accumulation type number According to long range prediction, LSTM shows clear superiority.
With increasing for monitoring information, prediction accuracy is gradually increased.LSTM prediction model remaining life is gradually at 75h Close to the real surplus life-span of gear, it can thus be appreciated that the invention patent can be with Accurate Prediction after the mutation of system degradation performance Gear remaining life, and necessary foundation is provided for adjustment preventive maintenance maintenance policy in time.
In conclusion the present invention is established according to the gear real-time monitoring information received based on LSTM Recognition with Recurrent Neural Network Fault time forecasting sequence model.Using random search preference pattern hyper parameter, Nesterov momentum optimizes gradient descent algorithm, Carry out regularized learning algorithm rate using the RMSProp algorithm of Nesterov momentum, by the life search of deep learning frame application to equipment In, by gear fatigue life verification experimental verification, its prediction model can faster be tracked the dynamic change of system, be improved Real-time predicting residual useful life accuracy.
The embodiment of the present invention is explained in detail above in conjunction with attached drawing, but the present invention is not limited to above-mentioned implementations Example, within the knowledge of a person skilled in the art, can also make without departing from the purpose of the present invention Various change out.

Claims (8)

1. the gear method for predicting residual useful life based on shot and long term memory network, which is characterized in that specific step is as follows:
Step 1 installs sensor in gear-box, obtains the Real-time Monitoring Data of tooth bending fatigue；
Acceleration transducer is mounted on the bearing block position of main examination case, the mounting temperature sensor in gear-box, in main examination case Noise transducer is installed in surface,
Step 2 carries out feature extraction to the fatigue state of gear, carries out slump evaluations to gear wear degraded performance；
Gear wear condition is changed using square amplitude and carries out performance slump evaluations, in each sampling time Δ t/length, The square amplitude of the time series of discrete random signal may be expressed as:
Δ t is using the time in formula；N=Fs× Δ t, FsFor sample frequency, n is sampling number, and ∑ indicates to sum, i ∈ (1,2, 3....n), xiIt (t) is sampled value；
Step 3, the Real-time Monitoring Data prediction model for establishing the tooth bending fatigue based on LSTM；
Step 4, the carry out parameter based on LSTM prediction model are preferred；
Step 5 carries out gear predicting residual useful life according to gear condition estimation and known gear distress threshold value:
Data of the vibration data that acceleration transducer receives after feature extraction are as mode input, using modified LSTM model carries out gear predicting residual useful life, can by the degenerate state value and known degenerate state fault threshold that predict The time for reaching fault threshold for the first time is solved, sampling number is converted to the time, quotes absolute error percentage (Absolute Percentage Error,APE)
Wherein, TRULFor the remaining life of model prediction,
Ta=T*-tp
In formula, tpIndicate current time, TaFor the real surplus life-span of gear, T*Reach the time of fault threshold for the first time for gear.
2. the gear method for predicting residual useful life according to claim 1 based on shot and long term memory network, it is characterised in that: The Real-time Monitoring Data prediction model for the tooth bending fatigue based on LSTM established in step 3 includes input layer, hidden layer, defeated Five layer, network training and neural network forecast functional modules out；
Input layer is responsible for carrying out preliminary treatment to the Real-time Monitoring Data of tooth bending fatigue to meet network inputs requirement, hides Layer builds single layer Recognition with Recurrent Neural Network using LSTM cell, and output layer provides prediction result, and network training is used from network structure Build, learning rate, window setting is upper optimizes to LSTM, and addition Nesterov momentum when parameters revision prevents from falling into office Portion is optimal, and using the optimization method of RMSProp algorithm correction model adaptive rate, neural network forecast is pre- point by point using the method for iteration It surveys, and then improves the accuracy of network model prediction.
3. the gear method for predicting residual useful life according to claim 2 based on shot and long term memory network, it is characterised in that: It includes RNN formula circulation that the LSTM cell that the hidden layer uses, which builds single layer Recognition with Recurrent Neural Network, while in itself " cell " Portion's circulation, each unit phase input and output, while the door control unit system including more parameters and control information flow, i.e., State cell(moment t, cell i), external input door (external input gate) forget door (forget gate), Out gate (output gate)；
Forget door to be responsible for removing the unessential information of LSTM study from " cell ", these information will be moved by door control unit operation It removes, forgets door and take two input ht-1And xt。ht-1For the state value of previous unit, xtFor the value of the t moment of list entries x, add Add bias term to input Sigmoid function.Sigmoid function value range is [0,1], if value is zero, forgets door and allows for Location mode forgets the information；
Corresponding propagated forward algorithm is as follows:
Input gate:
Forget door:
Cell:
Out gate:
Cell output:
Backpropagation calculates as follows:
Cell output:
Out gate:
State:
Cell:
Forget door:
Input gate:
Wherein ∑ indicates summation,Current input vector, subscript l, φ, c, ω respectively indicate indicate input gate, forget door, The relevant parameter of cell state unit, out gate.wil、wiφ、wicAnd wiwIt respectively indicates from being input to input gate, forget door, thin The weight of born of the same parents' state cell, out gate, whl、whφ、whcAnd whwIt respectively indicates from hidden layer to input gate, forget door, cell state The weight of unit, out gate, wcl、wcφAnd wcwIt respectively indicates from cell state to input gate, the weight of forgetting door and out gate, atIndicate the input of t moment, btIndicate the output of t moment,Indicate the state of t moment cell, g and h respectively indicate the defeated of cell Enter and export activation primitive, I indicates the number of the neuron of input layer, and K indicates that the neuron number of output layer, H indicate hidden layer Number of cells, σ are sigmoid functions.Indicate the cell output of t moment,Indicate the state of t moment, ζ indicates loss letter Number, δtIndicate gradient error, wckAnd wcgIt respectively indicates from cell state to output layer and the weight of hidden layer.
4. the gear method for predicting residual useful life according to claim 2 based on shot and long term memory network, it is characterised in that: The method of the network training is as follows:
First in input layer, the Real-time Monitoring Data for defining tooth bending fatigue is F0={ f1,f2,…,fn, then the instruction divided F can be expressed as by practicing collection and test settr={ f1,f2,…,fmAnd Fte={ fm+1,fm+2,…,fn, meet constraint condition m < n And m, n ∈；
Then to the element f in training settIt is standardized, uses classical z-score standardization formula (mean value 0, standard Difference is 1, is expressed as zscore), the training set after standardization can indicate are as follows:
Ftr'={ f1',f2',…,fm'}
Meet:
1≤t≤m,t∈
The characteristics of in order to adapt to hidden layer input, the method f ' divided using datatrIt is handled, setting split window length takes Value is L, then the mode input after dividing are as follows:
X={ X1,X2,…,XL}
Xp={ fp',fp+1',…,fm-L+p-1'}
Meet:
1≤p≤L；p,L∈N
Corresponding output are as follows:
Y={ Y1,Y2,…YL}
Yp={ fp+1',fp+2',…,fm-L+p'}
Next, X is inputted hidden layer, since hidden layer includes the L isomorphism LSTM cells by the connection of front and back moment, process is hidden Output after hiding layer can indicate are as follows:
P={ P1,P2,…,PL}
Pp=LSTMforward(Xp,Cp-1,Hp-1)
Wherein, Cp-1And Hp-1The state and output of respectively previous LSTM cell, LSTMforwardTo meter before expression LSTM cell Calculation method sets cell state vector magnitude as Sstate, then Cp-1And Hp-12 vector magnitudes are Sstate, hidden layer P, model Input X and theoretical output Y is the two-dimensional array that dimension is (m-L, L), selects mean square error as error calculation formula, training The loss function of process can be with is defined as:
The minimum optimization aim of loss function is set, the train epochs steps of netinit is given, changes using RMSProp Learning rate, Nesterov introduce momentum and change gradient, change update mode in terms of two, and then obtain final hidden layer net Network；
Finally, optimizing loss function using stochastic gradient descent, stochastic gradient descent is small quantities of according to data generation distribution extraction m (independent identically distributed) sample is measured, iteration only calculates the loss of a sample each time, then traverses all samples again, completes one The calculating of wheel.By calculating their gradient mean value, the unbiased esti-mator of available gradient is expressed as follows
Wherein g is gradient, and θ is the initial parameter of LSTM model forward calculation,It is gradient element, indicatesLocal derviation is asked to θ, using the sample from small lot m, stochastic gradient descent algorithm is declined using following gradient Estimation
θ←θ-ηg
Wherein η is learning rate, and L is the loss of each sample
L (x, y, θ)=- logp (y | x；θ)
Wherein, logarithm is sought in log expression.
5. the gear method for predicting residual useful life according to claim 2 based on shot and long term memory network, it is characterised in that: Specific step is as follows for the Nesterov momentum parameter optimization:
Assuming that learning rate η, momentum parameter α, initial rate v
It is iterated process every time:
1. extracting the small lot sample of { x (1), x (2) ... ..x (m) } from training set, wherein xtCorresponding output yt,
2. calculate gradient and Loss, v and θ are updated with this:
Calculate gradient:
It is interim to update:
θ←θ+v。
6. the gear method for predicting residual useful life according to claim 2 based on shot and long term memory network, it is characterised in that: Specific step is as follows for the RMSProp variable learning rate arithmetic: assuming that learning rate η, momentum parameter α, initial rate v, Rate of decay ρ initializes cumulant variable γ=0
It is iterated process every time:
1. extracting the small lot sample of { x (1), x (2) ... ..x (m) } from training set, wherein xtCorresponding output yt,
2. calculating gradient and Loss, γ is updated, calculates and updates in conjunction with gradient:
Calculate gradient:
It is interim to update:
θ←θ+v。
7. the gear method for predicting residual useful life according to claim 2 based on shot and long term memory network, it is characterised in that: Modified LSTM network (is expressed as) prediction process are as follows:
Firstly, the last line data of output Y are as follows:
Yf={ fm-L+1',fm-L+2',…,fm'}
Y is inputtedOutput result can indicate are as follows:
Then the predicted value at m+1 moment is pm+1.Then, by YfRear L-1 data point and pm+1Merge into new data line:
Yf+1={ fm-L+2',fm-L+3',…,pm+1}
By Yf+1InputThen the predicted value at m+2 moment is pm+2, and so on, obtained forecasting sequence are as follows:
Po={ pm+1,pm+2,…pn}
Next, by P0It carries out z-score instead to standardize and (be expressed as de_zscore), obtain final with test set Fte Corresponding forecasting sequence are as follows:
Wherein: m+1≤k≤n, k ∈ N.
Similarly, available with training set F using every a line of X as mode inputtrCorresponding fitting sequence Ptr.Finally, logical Cross calculating FtrAnd PtrAnd FteAnd PteDeviation quantitatively provide fitting and the precision of prediction of model.
8. the gear method for predicting residual useful life according to claim 1 based on shot and long term memory network, it is characterised in that: It is preferred to the carry out parameter based on LSTM prediction model by the way of random search in the step 3.
CN201811096152.0A 2018-09-19 2018-09-19 Gear method for predicting residual useful life based on shot and long term memory network Pending CN109343505A (en)

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Application publication date: 20190215